Ovi Paul
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ovi Paul has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- A. K. M. Mahbubur Rahman
Center for Computational & Data Sciences (CCDS) Independent University Bangladesh
1 shared publication - Abu Bakar Siddik Nayem
Department of Computer Science and Engineering, The Independent University Bangladesh, Dhaka 1229, Bangladesh
1 shared publication - Ali Shokri
University of Houston, Houston, USA
1 shared publication - Anis Sarker
Department of Computer Science and Engineering, The Independent University Bangladesh, Dhaka 1229, Bangladesh
1 shared publication Craig L. Glennie
Department of Civil and Environmental Engineering, United States
1 shared publication- Moinul Zaber
University of Dhaka
1 shared publication
Latest Publications
- Open Access
- 3 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 186-191
RepairFormer: Automated Repair of Structured Inputs using Transformers
- 3 October 2026
- Published by Association for Computing Machinery (ACM)
- p. 186-191
Open AccessRepairFormer: Automated Repair of Structured Inputs using Transformers
- Open Access
- 15 August 2025
- Published by Frontiers Media SA in Frontiers in Remote Sensing
- Vol. 6, 1521446
Automated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation
The study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classifications are then used to apply depth correction to all points within the water column. The study aimed to classify the scene into four classes: river surface, riverbed, ground, and other (for points outside of those three classes), focusing on the river surface and riverbed classes. To achieve this, PointCNN, a convolutional neural network model adept at handling unorganized and unstructured data in 3D space was implemented. The model was trained with airborne bathymetric lidar data from the Swan River in Montana and the Eel River in California. The model was tested on the Snake River in Wyoming to evaluate its performance. These diverse bathymetric datasets across the United States provided a solid foundation for the model’s robust testing. The results were strong for river surface classification, achieving an Intersection over Union of (0.89) and a Kappa coefficient of (0.92), indicating high reliability and minimal errors. The riverbed classification also showed an IoU of (0.7) and a slightly higher Kappa score of (0.76). Depth correction was then performed on riverbed points, proportional to the calculated depth from a surface model formed by Delaunay triangulation of ground and river surface points. The automated process performs significantly faster than traditional manual classification and depth correction processes, saving time and expense. Finally, corrected depths were quantitatively validated by comparing with independent Acoustic Doppler Current Profiler measurements from the Snake River, obtaining a mean depth error of 2 cm and an Root mean square error of 16 cm. These validation results show the reliability and accuracy of the proposed automated bathymetric depth correction workflow.
- 15 August 2025
- Published by Frontiers Media SA in Frontiers in Remote Sensing
- Vol. 6, 1521446
Open AccessAutomated depth correction of bathymetric LiDAR point clouds using PointCNN semantic segmentation
The study explores deep learning to perform direct semantic segmentation of bathymetric lidar points to improve bathymetry mapping. Focusing on river bathymetry, the goal is to accurately and simultaneously classify points on the benthic layer, water surface, and ground near riverbanks. These classifications are then used to apply depth correction to all points within the water column. The study aimed to classify the scene into four classes: river surface, riverbed, ground, and other (for points outside of those three classes), focusing on the river surface and riverbed classes. To achieve this, PointCNN, a convolutional neural network model adept at handling unorganized and unstructured data in 3D space was implemented. The model was trained with airborne bathymetric lidar data from the Swan River in Montana and the Eel River in California. The model was tested on the Snake River in Wyoming to evaluate its performance. These diverse bathymetric datasets across the United States provided a solid foundation for the model’s robust testing. The results were strong for river surface classification, achieving an Intersection over Union of (0.89) and a Kappa coefficient of (0.92), indicating high reliability and minimal errors. The riverbed classification also showed an IoU of (0.7) and a slightly higher Kappa score of (0.76). Depth correction was then performed on riverbed points, proportional to the calculated depth from a surface model formed by Delaunay triangulation of ground and river surface points. The automated process performs significantly faster than traditional manual classification and depth correction processes, saving time and expense. Finally, corrected depths were quantitatively validated by comparing with independent Acoustic Doppler Current Profiler measurements from the Snake River, obtaining a mean depth error of 2 cm and an Root mean square error of 16 cm. These validation results show the reliability and accuracy of the proposed automated bathymetric depth correction workflow.
- Open Access
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World
This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing of the urban space was used to train the models, and the remaining was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of for Dhaka, for Nairobi, for Jakarta, for Guangzhou city, for Mumbai, for Cairo, and for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.Open AccessApplying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World
This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing of the urban space was used to train the models, and the remaining was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of for Dhaka, for Nairobi, for Jakarta, for Guangzhou city, for Mumbai, for Cairo, and for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.